Overview

Dataset statistics

Number of variables22
Number of observations2240
Missing cells24
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory385.1 KiB
Average record size in memory176.1 B

Variable types

Numeric15
Categorical7

Alerts

Dt_Customer has a high cardinality: 663 distinct valuesHigh cardinality
Income is highly overall correlated with MntWines and 9 other fieldsHigh correlation
MntWines is highly overall correlated with Income and 8 other fieldsHigh correlation
MntFruits is highly overall correlated with Income and 7 other fieldsHigh correlation
MntMeatProducts is highly overall correlated with Income and 8 other fieldsHigh correlation
MntFishProducts is highly overall correlated with Income and 7 other fieldsHigh correlation
MntSweetProducts is highly overall correlated with Income and 7 other fieldsHigh correlation
MntGoldProds is highly overall correlated with Income and 8 other fieldsHigh correlation
NumWebPurchases is highly overall correlated with Income and 5 other fieldsHigh correlation
NumCatalogPurchases is highly overall correlated with Income and 9 other fieldsHigh correlation
NumStorePurchases is highly overall correlated with Income and 8 other fieldsHigh correlation
NumWebVisitsMonth is highly overall correlated with Income and 1 other fieldsHigh correlation
Complain is highly imbalanced (92.3%)Imbalance
Income has 24 (1.1%) missing valuesMissing
Id has unique valuesUnique
Recency has 28 (1.2%) zerosZeros
MntFruits has 400 (17.9%) zerosZeros
MntFishProducts has 384 (17.1%) zerosZeros
MntSweetProducts has 419 (18.7%) zerosZeros
MntGoldProds has 61 (2.7%) zerosZeros
NumDealsPurchases has 46 (2.1%) zerosZeros
NumWebPurchases has 49 (2.2%) zerosZeros
NumCatalogPurchases has 586 (26.2%) zerosZeros

Reproduction

Analysis started2023-08-26 15:30:26.120914
Analysis finished2023-08-26 15:31:14.120978
Duration48 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Id
Real number (ℝ)

Distinct2240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5592.1598
Minimum0
Maximum11191
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:14.313663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile576.85
Q12828.25
median5458.5
Q38427.75
95-th percentile10675.05
Maximum11191
Range11191
Interquartile range (IQR)5599.5

Descriptive statistics

Standard deviation3246.6622
Coefficient of variation (CV)0.58057393
Kurtosis-1.190028
Mean5592.1598
Median Absolute Deviation (MAD)2791
Skewness0.039831873
Sum12526438
Variance10540815
MonotonicityNot monotonic
2023-08-26T12:31:14.678203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1826 1
 
< 0.1%
5680 1
 
< 0.1%
4640 1
 
< 0.1%
2525 1
 
< 0.1%
9503 1
 
< 0.1%
10704 1
 
< 0.1%
2669 1
 
< 0.1%
10037 1
 
< 0.1%
3726 1
 
< 0.1%
10872 1
 
< 0.1%
Other values (2230) 2230
99.6%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
35 1
< 0.1%
ValueCountFrequency (%)
11191 1
< 0.1%
11188 1
< 0.1%
11187 1
< 0.1%
11181 1
< 0.1%
11178 1
< 0.1%
11176 1
< 0.1%
11171 1
< 0.1%
11166 1
< 0.1%
11148 1
< 0.1%
11133 1
< 0.1%

Year_Birth
Real number (ℝ)

Distinct59
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.8058
Minimum1893
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:15.032247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1893
5-th percentile1950
Q11959
median1970
Q31977
95-th percentile1988
Maximum1996
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.984069
Coefficient of variation (CV)0.0060869739
Kurtosis0.71746444
Mean1968.8058
Median Absolute Deviation (MAD)9
Skewness-0.34994386
Sum4410125
Variance143.61792
MonotonicityNot monotonic
2023-08-26T12:31:15.412946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1976 89
 
4.0%
1971 87
 
3.9%
1975 83
 
3.7%
1972 79
 
3.5%
1970 77
 
3.4%
1978 77
 
3.4%
1965 74
 
3.3%
1973 74
 
3.3%
1969 71
 
3.2%
1974 69
 
3.1%
Other values (49) 1460
65.2%
ValueCountFrequency (%)
1893 1
 
< 0.1%
1899 1
 
< 0.1%
1900 1
 
< 0.1%
1940 1
 
< 0.1%
1941 1
 
< 0.1%
1943 7
0.3%
1944 7
0.3%
1945 8
0.4%
1946 16
0.7%
1947 16
0.7%
ValueCountFrequency (%)
1996 2
 
0.1%
1995 5
 
0.2%
1994 3
 
0.1%
1993 5
 
0.2%
1992 13
0.6%
1991 15
0.7%
1990 18
0.8%
1989 30
1.3%
1988 29
1.3%
1987 27
1.2%

Education
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Graduation
1127 
PhD
486 
Master
370 
2n Cycle
203 
Basic
 
54

Length

Max length10
Median length10
Mean length7.51875
Min length3

Characters and Unicode

Total characters16842
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowGraduation
4th rowGraduation
5th rowGraduation

Common Values

ValueCountFrequency (%)
Graduation 1127
50.3%
PhD 486
21.7%
Master 370
 
16.5%
2n Cycle 203
 
9.1%
Basic 54
 
2.4%

Length

2023-08-26T12:31:15.768392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-26T12:31:16.070612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
graduation 1127
46.1%
phd 486
19.9%
master 370
 
15.1%
2n 203
 
8.3%
cycle 203
 
8.3%
basic 54
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a 2678
15.9%
r 1497
8.9%
t 1497
8.9%
n 1330
 
7.9%
i 1181
 
7.0%
G 1127
 
6.7%
d 1127
 
6.7%
u 1127
 
6.7%
o 1127
 
6.7%
e 573
 
3.4%
Other values (12) 3578
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13710
81.4%
Uppercase Letter 2726
 
16.2%
Decimal Number 203
 
1.2%
Space Separator 203
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2678
19.5%
r 1497
10.9%
t 1497
10.9%
n 1330
9.7%
i 1181
8.6%
d 1127
8.2%
u 1127
8.2%
o 1127
8.2%
e 573
 
4.2%
h 486
 
3.5%
Other values (4) 1087
7.9%
Uppercase Letter
ValueCountFrequency (%)
G 1127
41.3%
D 486
17.8%
P 486
17.8%
M 370
 
13.6%
C 203
 
7.4%
B 54
 
2.0%
Decimal Number
ValueCountFrequency (%)
2 203
100.0%
Space Separator
ValueCountFrequency (%)
203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16436
97.6%
Common 406
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2678
16.3%
r 1497
9.1%
t 1497
9.1%
n 1330
8.1%
i 1181
 
7.2%
G 1127
 
6.9%
d 1127
 
6.9%
u 1127
 
6.9%
o 1127
 
6.9%
e 573
 
3.5%
Other values (10) 3172
19.3%
Common
ValueCountFrequency (%)
2 203
50.0%
203
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2678
15.9%
r 1497
8.9%
t 1497
8.9%
n 1330
 
7.9%
i 1181
 
7.0%
G 1127
 
6.7%
d 1127
 
6.7%
u 1127
 
6.7%
o 1127
 
6.7%
e 573
 
3.4%
Other values (12) 3578
21.2%

Marital_Status
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Married
864 
Together
580 
Single
480 
Divorced
232 
Widow
 
77
Other values (3)
 
7

Length

Max length8
Median length7
Mean length7.0732143
Min length4

Characters and Unicode

Total characters15844
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDivorced
2nd rowSingle
3rd rowMarried
4th rowTogether
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 864
38.6%
Together 580
25.9%
Single 480
21.4%
Divorced 232
 
10.4%
Widow 77
 
3.4%
Alone 3
 
0.1%
YOLO 2
 
0.1%
Absurd 2
 
0.1%

Length

2023-08-26T12:31:16.294747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-26T12:31:16.529396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
married 864
38.6%
together 580
25.9%
single 480
21.4%
divorced 232
 
10.4%
widow 77
 
3.4%
alone 3
 
0.1%
yolo 2
 
0.1%
absurd 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 2739
17.3%
r 2542
16.0%
i 1653
10.4%
d 1175
7.4%
g 1060
 
6.7%
o 892
 
5.6%
M 864
 
5.5%
a 864
 
5.5%
T 580
 
3.7%
t 580
 
3.7%
Other values (16) 2895
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13598
85.8%
Uppercase Letter 2246
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2739
20.1%
r 2542
18.7%
i 1653
12.2%
d 1175
8.6%
g 1060
 
7.8%
o 892
 
6.6%
a 864
 
6.4%
t 580
 
4.3%
h 580
 
4.3%
n 483
 
3.6%
Other values (7) 1030
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
M 864
38.5%
T 580
25.8%
S 480
21.4%
D 232
 
10.3%
W 77
 
3.4%
A 5
 
0.2%
O 4
 
0.2%
Y 2
 
0.1%
L 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 15844
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2739
17.3%
r 2542
16.0%
i 1653
10.4%
d 1175
7.4%
g 1060
 
6.7%
o 892
 
5.6%
M 864
 
5.5%
a 864
 
5.5%
T 580
 
3.7%
t 580
 
3.7%
Other values (16) 2895
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2739
17.3%
r 2542
16.0%
i 1653
10.4%
d 1175
7.4%
g 1060
 
6.7%
o 892
 
5.6%
M 864
 
5.5%
a 864
 
5.5%
T 580
 
3.7%
t 580
 
3.7%
Other values (16) 2895
18.3%

Income
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1974
Distinct (%)89.1%
Missing24
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean52247.251
Minimum1730
Maximum666666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:16.764669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile18985.5
Q135303
median51381.5
Q368522
95-th percentile84130
Maximum666666
Range664936
Interquartile range (IQR)33219

Descriptive statistics

Standard deviation25173.077
Coefficient of variation (CV)0.48180672
Kurtosis159.6367
Mean52247.251
Median Absolute Deviation (MAD)16557.5
Skewness6.7634874
Sum1.1577991 × 108
Variance6.3368379 × 108
MonotonicityNot monotonic
2023-08-26T12:31:16.987184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500 12
 
0.5%
35860 4
 
0.2%
80134 3
 
0.1%
63841 3
 
0.1%
47025 3
 
0.1%
34176 3
 
0.1%
37760 3
 
0.1%
48432 3
 
0.1%
83844 3
 
0.1%
46098 3
 
0.1%
Other values (1964) 2176
97.1%
(Missing) 24
 
1.1%
ValueCountFrequency (%)
1730 1
< 0.1%
2447 1
< 0.1%
3502 1
< 0.1%
4023 1
< 0.1%
4428 1
< 0.1%
4861 1
< 0.1%
5305 1
< 0.1%
5648 1
< 0.1%
6560 1
< 0.1%
6835 1
< 0.1%
ValueCountFrequency (%)
666666 1
< 0.1%
162397 1
< 0.1%
160803 1
< 0.1%
157733 1
< 0.1%
157243 1
< 0.1%
157146 1
< 0.1%
156924 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
105471 1
< 0.1%

Kidhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1293 
1
899 
2
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Length

2023-08-26T12:31:17.182090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-26T12:31:17.360288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Teenhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1158 
1
1030 
2
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Length

2023-08-26T12:31:17.549757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-26T12:31:17.729661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Dt_Customer
Categorical

Distinct663
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
8/31/2012
 
12
12/9/2012
 
11
2/14/2013
 
11
12/5/2014
 
11
8/20/2013
 
10
Other values (658)
2185 

Length

Max length10
Median length9
Mean length8.965625
Min length8

Characters and Unicode

Total characters20083
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)4.8%

Sample

1st row6/16/2014
2nd row6/15/2014
3rd row5/13/2014
4th row11/5/2014
5th row8/4/2014

Common Values

ValueCountFrequency (%)
8/31/2012 12
 
0.5%
12/9/2012 11
 
0.5%
2/14/2013 11
 
0.5%
12/5/2014 11
 
0.5%
8/20/2013 10
 
0.4%
5/22/2014 10
 
0.4%
2/1/2013 9
 
0.4%
10/29/2012 9
 
0.4%
5/4/2014 9
 
0.4%
3/23/2014 9
 
0.4%
Other values (653) 2139
95.5%

Length

2023-08-26T12:31:17.934418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8/31/2012 12
 
0.5%
2/14/2013 11
 
0.5%
12/5/2014 11
 
0.5%
12/9/2012 11
 
0.5%
8/20/2013 10
 
0.4%
5/22/2014 10
 
0.4%
5/4/2014 9
 
0.4%
1/3/2014 9
 
0.4%
3/23/2014 9
 
0.4%
10/29/2012 9
 
0.4%
Other values (653) 2139
95.5%

Most occurring characters

ValueCountFrequency (%)
/ 4480
22.3%
1 4228
21.1%
2 4076
20.3%
0 2663
13.3%
3 1742
 
8.7%
4 928
 
4.6%
8 445
 
2.2%
5 424
 
2.1%
9 403
 
2.0%
6 363
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15603
77.7%
Other Punctuation 4480
 
22.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4228
27.1%
2 4076
26.1%
0 2663
17.1%
3 1742
11.2%
4 928
 
5.9%
8 445
 
2.9%
5 424
 
2.7%
9 403
 
2.6%
6 363
 
2.3%
7 331
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/ 4480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20083
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 4480
22.3%
1 4228
21.1%
2 4076
20.3%
0 2663
13.3%
3 1742
 
8.7%
4 928
 
4.6%
8 445
 
2.2%
5 424
 
2.1%
9 403
 
2.0%
6 363
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20083
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 4480
22.3%
1 4228
21.1%
2 4076
20.3%
0 2663
13.3%
3 1742
 
8.7%
4 928
 
4.6%
8 445
 
2.2%
5 424
 
2.1%
9 403
 
2.0%
6 363
 
1.8%

Recency
Real number (ℝ)

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.109375
Minimum0
Maximum99
Zeros28
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:18.346998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.962453
Coefficient of variation (CV)0.58975405
Kurtosis-1.2018968
Mean49.109375
Median Absolute Deviation (MAD)25
Skewness-0.0019866586
Sum110005
Variance838.82367
MonotonicityIncreasing
2023-08-26T12:31:18.634894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 37
 
1.7%
54 32
 
1.4%
30 32
 
1.4%
46 31
 
1.4%
49 30
 
1.3%
65 30
 
1.3%
92 30
 
1.3%
3 29
 
1.3%
71 29
 
1.3%
29 29
 
1.3%
Other values (90) 1931
86.2%
ValueCountFrequency (%)
0 28
1.2%
1 24
1.1%
2 28
1.2%
3 29
1.3%
4 27
1.2%
5 15
0.7%
6 21
0.9%
7 12
0.5%
8 25
1.1%
9 24
1.1%
ValueCountFrequency (%)
99 17
0.8%
98 22
1.0%
97 20
0.9%
96 25
1.1%
95 19
0.8%
94 26
1.2%
93 21
0.9%
92 30
1.3%
91 18
0.8%
90 20
0.9%

MntWines
Real number (ℝ)

Distinct776
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.93571
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:18.911828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123.75
median173.5
Q3504.25
95-th percentile1000
Maximum1493
Range1493
Interquartile range (IQR)480.5

Descriptive statistics

Standard deviation336.59739
Coefficient of variation (CV)1.1074625
Kurtosis0.59874359
Mean303.93571
Median Absolute Deviation (MAD)164.5
Skewness1.1757706
Sum680816
Variance113297.8
MonotonicityNot monotonic
2023-08-26T12:31:19.226318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 42
 
1.9%
5 40
 
1.8%
6 37
 
1.7%
1 37
 
1.7%
4 33
 
1.5%
3 30
 
1.3%
8 30
 
1.3%
9 28
 
1.2%
12 25
 
1.1%
14 24
 
1.1%
Other values (766) 1914
85.4%
ValueCountFrequency (%)
0 13
 
0.6%
1 37
1.7%
2 42
1.9%
3 30
1.3%
4 33
1.5%
5 40
1.8%
6 37
1.7%
7 22
1.0%
8 30
1.3%
9 28
1.2%
ValueCountFrequency (%)
1493 1
< 0.1%
1492 2
0.1%
1486 1
< 0.1%
1478 2
0.1%
1462 1
< 0.1%
1459 1
< 0.1%
1449 1
< 0.1%
1396 1
< 0.1%
1394 1
< 0.1%
1379 1
< 0.1%

MntFruits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.302232
Minimum0
Maximum199
Zeros400
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:19.540654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile123
Maximum199
Range199
Interquartile range (IQR)32

Descriptive statistics

Standard deviation39.773434
Coefficient of variation (CV)1.5121695
Kurtosis4.0509763
Mean26.302232
Median Absolute Deviation (MAD)8
Skewness2.1020633
Sum58917
Variance1581.926
MonotonicityNot monotonic
2023-08-26T12:31:19.909739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 400
 
17.9%
1 162
 
7.2%
2 120
 
5.4%
3 116
 
5.2%
4 104
 
4.6%
7 67
 
3.0%
5 65
 
2.9%
6 62
 
2.8%
12 50
 
2.2%
8 48
 
2.1%
Other values (148) 1046
46.7%
ValueCountFrequency (%)
0 400
17.9%
1 162
7.2%
2 120
 
5.4%
3 116
 
5.2%
4 104
 
4.6%
5 65
 
2.9%
6 62
 
2.8%
7 67
 
3.0%
8 48
 
2.1%
9 35
 
1.6%
ValueCountFrequency (%)
199 2
0.1%
197 1
 
< 0.1%
194 3
0.1%
193 2
0.1%
190 1
 
< 0.1%
189 1
 
< 0.1%
185 2
0.1%
184 1
 
< 0.1%
183 3
0.1%
181 1
 
< 0.1%

MntMeatProducts
Real number (ℝ)

Distinct558
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.95
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:20.204501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median67
Q3232
95-th percentile687.1
Maximum1725
Range1725
Interquartile range (IQR)216

Descriptive statistics

Standard deviation225.71537
Coefficient of variation (CV)1.3519938
Kurtosis5.5167241
Mean166.95
Median Absolute Deviation (MAD)59
Skewness2.0832331
Sum373968
Variance50947.429
MonotonicityNot monotonic
2023-08-26T12:31:20.437240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 53
 
2.4%
5 50
 
2.2%
11 49
 
2.2%
8 46
 
2.1%
6 43
 
1.9%
10 40
 
1.8%
3 40
 
1.8%
9 38
 
1.7%
16 36
 
1.6%
12 35
 
1.6%
Other values (548) 1810
80.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14
 
0.6%
2 30
1.3%
3 40
1.8%
4 30
1.3%
5 50
2.2%
6 43
1.9%
7 53
2.4%
8 46
2.1%
9 38
1.7%
ValueCountFrequency (%)
1725 2
0.1%
1622 1
< 0.1%
1607 1
< 0.1%
1582 1
< 0.1%
984 1
< 0.1%
981 1
< 0.1%
974 1
< 0.1%
968 1
< 0.1%
961 1
< 0.1%
951 2
0.1%

MntFishProducts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct182
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.525446
Minimum0
Maximum259
Zeros384
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:20.702049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile168.05
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.628979
Coefficient of variation (CV)1.4557849
Kurtosis3.0964609
Mean37.525446
Median Absolute Deviation (MAD)12
Skewness1.919769
Sum84057
Variance2984.3254
MonotonicityNot monotonic
2023-08-26T12:31:21.045876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 384
 
17.1%
2 156
 
7.0%
3 130
 
5.8%
4 108
 
4.8%
6 82
 
3.7%
7 66
 
2.9%
8 58
 
2.6%
10 55
 
2.5%
13 48
 
2.1%
12 47
 
2.1%
Other values (172) 1106
49.4%
ValueCountFrequency (%)
0 384
17.1%
1 10
 
0.4%
2 156
7.0%
3 130
 
5.8%
4 108
 
4.8%
5 1
 
< 0.1%
6 82
 
3.7%
7 66
 
2.9%
8 58
 
2.6%
10 55
 
2.5%
ValueCountFrequency (%)
259 1
 
< 0.1%
258 3
0.1%
254 1
 
< 0.1%
253 1
 
< 0.1%
250 3
0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
242 1
 
< 0.1%
240 2
0.1%
237 2
0.1%

MntSweetProducts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct177
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.062946
Minimum0
Maximum263
Zeros419
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:21.384591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile126
Maximum263
Range263
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.280498
Coefficient of variation (CV)1.5253512
Kurtosis4.3765483
Mean27.062946
Median Absolute Deviation (MAD)8
Skewness2.1360807
Sum60621
Variance1704.0796
MonotonicityNot monotonic
2023-08-26T12:31:21.636856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 419
 
18.7%
1 161
 
7.2%
2 128
 
5.7%
3 101
 
4.5%
4 82
 
3.7%
5 65
 
2.9%
6 64
 
2.9%
7 57
 
2.5%
8 56
 
2.5%
12 45
 
2.0%
Other values (167) 1062
47.4%
ValueCountFrequency (%)
0 419
18.7%
1 161
 
7.2%
2 128
 
5.7%
3 101
 
4.5%
4 82
 
3.7%
5 65
 
2.9%
6 64
 
2.9%
7 57
 
2.5%
8 56
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
263 1
 
< 0.1%
262 1
 
< 0.1%
198 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 3
0.1%
192 3
0.1%
191 1
 
< 0.1%
189 2
0.1%

MntGoldProds
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct213
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.021875
Minimum0
Maximum362
Zeros61
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:21.856528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24
Q356
95-th percentile165.05
Maximum362
Range362
Interquartile range (IQR)47

Descriptive statistics

Standard deviation52.167439
Coefficient of variation (CV)1.1850345
Kurtosis3.5517093
Mean44.021875
Median Absolute Deviation (MAD)18
Skewness1.8861056
Sum98609
Variance2721.4417
MonotonicityNot monotonic
2023-08-26T12:31:22.086638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 73
 
3.3%
4 70
 
3.1%
3 69
 
3.1%
5 63
 
2.8%
12 63
 
2.8%
2 62
 
2.8%
0 61
 
2.7%
6 57
 
2.5%
7 54
 
2.4%
10 49
 
2.2%
Other values (203) 1619
72.3%
ValueCountFrequency (%)
0 61
2.7%
1 73
3.3%
2 62
2.8%
3 69
3.1%
4 70
3.1%
5 63
2.8%
6 57
2.5%
7 54
2.4%
8 40
1.8%
9 44
2.0%
ValueCountFrequency (%)
362 1
< 0.1%
321 1
< 0.1%
291 1
< 0.1%
262 1
< 0.1%
249 1
< 0.1%
248 1
< 0.1%
247 1
< 0.1%
246 1
< 0.1%
245 1
< 0.1%
242 2
0.1%

NumDealsPurchases
Real number (ℝ)

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.325
Minimum0
Maximum15
Zeros46
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:22.281607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9322375
Coefficient of variation (CV)0.83106989
Kurtosis8.9369143
Mean2.325
Median Absolute Deviation (MAD)1
Skewness2.4185694
Sum5208
Variance3.7335418
MonotonicityNot monotonic
2023-08-26T12:31:22.465304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 970
43.3%
2 497
22.2%
3 297
 
13.3%
4 189
 
8.4%
5 94
 
4.2%
6 61
 
2.7%
0 46
 
2.1%
7 40
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
Other values (5) 24
 
1.1%
ValueCountFrequency (%)
0 46
 
2.1%
1 970
43.3%
2 497
22.2%
3 297
 
13.3%
4 189
 
8.4%
5 94
 
4.2%
6 61
 
2.7%
7 40
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
ValueCountFrequency (%)
15 7
 
0.3%
13 3
 
0.1%
12 4
 
0.2%
11 5
 
0.2%
10 5
 
0.2%
9 8
 
0.4%
8 14
 
0.6%
7 40
1.8%
6 61
2.7%
5 94
4.2%

NumWebPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0848214
Minimum0
Maximum27
Zeros49
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:22.646492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7787141
Coefficient of variation (CV)0.68025352
Kurtosis5.7031284
Mean4.0848214
Median Absolute Deviation (MAD)2
Skewness1.3827943
Sum9150
Variance7.7212523
MonotonicityNot monotonic
2023-08-26T12:31:22.829765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 373
16.7%
1 354
15.8%
3 336
15.0%
4 280
12.5%
5 220
9.8%
6 205
9.2%
7 155
6.9%
8 102
 
4.6%
9 75
 
3.3%
0 49
 
2.2%
Other values (5) 91
 
4.1%
ValueCountFrequency (%)
0 49
 
2.2%
1 354
15.8%
2 373
16.7%
3 336
15.0%
4 280
12.5%
5 220
9.8%
6 205
9.2%
7 155
6.9%
8 102
 
4.6%
9 75
 
3.3%
ValueCountFrequency (%)
27 2
 
0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
11 44
 
2.0%
10 43
 
1.9%
9 75
 
3.3%
8 102
4.6%
7 155
6.9%
6 205
9.2%
5 220
9.8%

NumCatalogPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6620536
Minimum0
Maximum28
Zeros586
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:22.993545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9231007
Coefficient of variation (CV)1.0980623
Kurtosis8.0474368
Mean2.6620536
Median Absolute Deviation (MAD)2
Skewness1.8809888
Sum5963
Variance8.5445174
MonotonicityNot monotonic
2023-08-26T12:31:23.158609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 586
26.2%
1 497
22.2%
2 276
12.3%
3 184
 
8.2%
4 182
 
8.1%
5 140
 
6.2%
6 128
 
5.7%
7 79
 
3.5%
8 55
 
2.5%
10 48
 
2.1%
Other values (4) 65
 
2.9%
ValueCountFrequency (%)
0 586
26.2%
1 497
22.2%
2 276
12.3%
3 184
 
8.2%
4 182
 
8.1%
5 140
 
6.2%
6 128
 
5.7%
7 79
 
3.5%
8 55
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
28 3
 
0.1%
22 1
 
< 0.1%
11 19
 
0.8%
10 48
 
2.1%
9 42
 
1.9%
8 55
 
2.5%
7 79
3.5%
6 128
5.7%
5 140
6.2%
4 182
8.1%

NumStorePurchases
Real number (ℝ)

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7901786
Minimum0
Maximum13
Zeros15
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:23.327757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2509581
Coefficient of variation (CV)0.56146077
Kurtosis-0.62204828
Mean5.7901786
Median Absolute Deviation (MAD)2
Skewness0.70223729
Sum12970
Variance10.568729
MonotonicityNot monotonic
2023-08-26T12:31:23.509028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 490
21.9%
4 323
14.4%
2 223
10.0%
5 212
9.5%
6 178
 
7.9%
8 149
 
6.7%
7 143
 
6.4%
10 125
 
5.6%
9 106
 
4.7%
12 105
 
4.7%
Other values (4) 186
 
8.3%
ValueCountFrequency (%)
0 15
 
0.7%
1 7
 
0.3%
2 223
10.0%
3 490
21.9%
4 323
14.4%
5 212
9.5%
6 178
 
7.9%
7 143
 
6.4%
8 149
 
6.7%
9 106
 
4.7%
ValueCountFrequency (%)
13 83
 
3.7%
12 105
 
4.7%
11 81
 
3.6%
10 125
 
5.6%
9 106
 
4.7%
8 149
6.7%
7 143
6.4%
6 178
7.9%
5 212
9.5%
4 323
14.4%

NumWebVisitsMonth
Real number (ℝ)

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3165179
Minimum0
Maximum20
Zeros11
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-08-26T12:31:23.865642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.426645
Coefficient of variation (CV)0.45643503
Kurtosis1.8216138
Mean5.3165179
Median Absolute Deviation (MAD)2
Skewness0.20792556
Sum11909
Variance5.888606
MonotonicityNot monotonic
2023-08-26T12:31:24.163156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 393
17.5%
8 342
15.3%
6 340
15.2%
5 281
12.5%
4 218
9.7%
3 205
9.2%
2 202
9.0%
1 153
 
6.8%
9 83
 
3.7%
0 11
 
0.5%
Other values (6) 12
 
0.5%
ValueCountFrequency (%)
0 11
 
0.5%
1 153
 
6.8%
2 202
9.0%
3 205
9.2%
4 218
9.7%
5 281
12.5%
6 340
15.2%
7 393
17.5%
8 342
15.3%
9 83
 
3.7%
ValueCountFrequency (%)
20 3
 
0.1%
19 2
 
0.1%
17 1
 
< 0.1%
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 83
 
3.7%
8 342
15.3%
7 393
17.5%
6 340
15.2%

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1906 
1
334 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Length

2023-08-26T12:31:24.490307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-26T12:31:24.746196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Complain
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2219 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Length

2023-08-26T12:31:25.026709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-26T12:31:25.280124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Interactions

2023-08-26T12:31:10.200382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:28.551965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:31.161803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:33.688827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:36.688659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:39.829672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:42.933577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:46.388499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:49.398280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:52.507518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:55.547285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:58.422578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:01.304991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:04.103428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:07.123925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:10.366602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:28.737923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:31.315262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:33.852232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:36.896011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:40.046185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:43.129148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:46.571026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:49.546543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:52.727691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:55.714124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:58.649430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:01.465977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:04.247813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:07.303198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:10.541893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:28.883425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:31.464316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:34.053460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:37.106296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:40.269068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:43.337608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:46.795036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:49.768100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:52.944389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:55.866278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:58.866820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:01.614074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:04.414964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:07.514066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:10.753634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:29.154655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:31.619460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:34.230579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:37.266827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:40.502634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:43.565490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:46.990355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:49.996063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:53.163318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:56.089955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:59.088373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:01.780007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:04.637579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:07.695778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:10.979130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:29.366548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:31.765224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:34.445537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:37.415620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:40.722968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:43.776337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:47.217495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:50.182632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:53.321368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:56.276367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:59.306190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:01.955789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:04.858864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:07.866299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:11.142734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:29.598389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:31.928931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:34.653587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:37.641344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:40.937033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:43.985105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:47.447177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:50.344947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:53.498566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:56.435762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:59.533931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:02.109367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:05.052147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:08.061823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:11.323429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:29.767257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:32.122728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:34.829375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:37.853639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:41.104817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:44.206603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:47.655644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:50.536304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:53.671887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:56.649850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:59.767865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:02.276608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:05.281028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:08.284766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:11.567082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:29.928785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:32.279406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:34.996727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:38.071674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:41.271194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:44.442140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:47.879169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:50.761183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:53.842235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:56.861641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:00.003733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:02.440344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:05.496493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:08.490669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:11.934125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:30.069006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:32.424906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:35.205646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:38.282845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:41.431617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:44.663285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:48.106625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:50.970545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:54.027581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:57.016417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:00.194117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:02.837363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:05.714928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:08.718035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:12.086868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:30.221301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:32.571403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:35.422248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:38.494475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:41.640152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:44.883588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:48.267582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:51.179125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:54.207862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:57.223614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:00.355869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:03.045928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:05.932381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:08.921418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:12.261104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:30.385000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:32.730291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:35.631709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:38.714151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:41.871024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:45.115646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:48.495410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:51.406337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:54.364490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:57.421722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:00.514597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:03.268698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:06.144188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:09.115869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:12.447069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:30.538833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:32.885365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:35.861171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:38.939270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:42.101440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:45.478144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:48.731079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:51.628361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:54.698488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:57.623752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:00.676721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:03.492949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:06.415051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:09.344446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:12.666635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:30.686417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:33.055313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:36.005022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:39.148294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:42.310256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:45.677093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:48.894756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:51.838915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:54.906362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:57.806575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:00.829754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:03.639881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:06.600637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:09.565605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:12.868255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:30.836971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:33.237933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:36.153867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:39.387458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:42.507615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:45.864576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:49.068450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:52.051289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:55.126754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:58.029866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:00.979399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:03.785260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:06.749559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:09.789891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:13.101794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:30.994653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:33.458007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:36.324286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:39.614492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:42.711843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:46.079427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:49.229396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:52.280716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:55.309658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:30:58.192941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:01.135437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:03.941877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:06.934051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-26T12:31:10.001118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-26T12:31:25.473842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IdYear_BirthIncomeRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthEducationMarital_StatusKidhomeTeenhomeResponseComplain
Id1.0000.0030.004-0.046-0.026-0.022-0.013-0.029-0.034-0.042-0.024-0.025-0.012-0.023-0.0110.0000.0040.0000.0000.0320.000
Year_Birth0.0031.000-0.217-0.021-0.234-0.025-0.112-0.0310.003-0.077-0.087-0.164-0.179-0.1680.1310.1130.0930.2210.3090.0000.137
Income0.004-0.2171.0000.0080.8300.5820.8170.5770.5670.506-0.1960.5730.7920.732-0.6440.0540.0000.2860.1740.1920.000
Recency-0.046-0.0210.0081.0000.0190.0250.0280.0130.0240.0180.008-0.0040.0310.006-0.0220.0000.0260.0700.0500.2080.000
MntWines-0.026-0.2340.8300.0191.0000.5180.8240.5250.5050.5750.0570.7400.8230.807-0.3890.1140.0190.4070.1170.2680.000
MntFruits-0.022-0.0250.5820.0250.5181.0000.7130.7050.6910.569-0.1100.4710.6350.583-0.4430.0700.0290.3120.1210.1520.000
MntMeatProducts-0.013-0.1120.8170.0280.8240.7131.0000.7260.6960.638-0.0320.6790.8520.779-0.4920.0540.0300.3220.2270.2430.000
MntFishProducts-0.029-0.0310.5770.0130.5250.7050.7261.0000.7010.565-0.1200.4660.6570.583-0.4580.0620.0530.3230.1380.1310.000
MntSweetProducts-0.0340.0030.5670.0240.5050.6910.6960.7011.0000.543-0.1060.4640.6280.581-0.4490.0680.0000.2920.1010.1130.000
MntGoldProds-0.042-0.0770.5060.0180.5750.5690.6380.5650.5431.0000.0900.5800.6490.540-0.2610.0660.0540.2640.0560.1380.000
NumDealsPurchases-0.024-0.087-0.1960.0080.057-0.110-0.032-0.120-0.1060.0901.0000.284-0.0400.1000.3980.0000.0190.2100.3470.0960.000
NumWebPurchases-0.025-0.1640.573-0.0040.7400.4710.6790.4660.4640.5800.2841.0000.6190.673-0.0970.0830.0390.2940.1600.1660.000
NumCatalogPurchases-0.012-0.1790.7920.0310.8230.6350.8520.6570.6280.649-0.0400.6191.0000.709-0.5360.0640.0000.3860.1190.2190.000
NumStorePurchases-0.023-0.1680.7320.0060.8070.5830.7790.5830.5810.5400.1000.6730.7091.000-0.4540.1040.0250.4030.0850.1490.000
NumWebVisitsMonth-0.0110.131-0.644-0.022-0.389-0.443-0.492-0.458-0.449-0.2610.398-0.097-0.536-0.4541.0000.0540.0000.3450.2170.1210.000
Education0.0000.1130.0540.0000.1140.0700.0540.0620.0680.0660.0000.0830.0640.1040.0541.0000.0000.0510.1040.0920.039
Marital_Status0.0040.0930.0000.0260.0190.0290.0300.0530.0000.0540.0190.0390.0000.0250.0000.0001.0000.0400.0730.1450.000
Kidhome0.0000.2210.2860.0700.4070.3120.3220.3230.2920.2640.2100.2940.3860.4030.3450.0510.0401.0000.0540.0750.027
Teenhome0.0000.3090.1740.0500.1170.1210.2270.1380.1010.0560.3470.1600.1190.0850.2170.1040.0730.0541.0000.1590.000
Response0.0320.0000.1920.2080.2680.1520.2430.1310.1130.1380.0960.1660.2190.1490.1210.0920.1450.0750.1591.0000.000
Complain0.0000.1370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0270.0000.0001.000

Missing values

2023-08-26T12:31:13.407893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-26T12:31:13.940133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IdYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthResponseComplain
018261970GraduationDivorced84835.0006/16/201401891043791111892181446110
111961GraduationSingle57091.0006/15/2014046456470371737510
2104761958GraduationMarried67267.0015/13/201401341159152301325200
313861967GraduationTogether32474.01111/5/2014010010001102700
453711989GraduationSingle21474.0108/4/2014061624110342312710
573481958PhDSingle71691.0003/17/2014033613041124032431475210
6407319542n CycleMarried63564.0001/29/2014076980252153465110107610
719911967GraduationTogether44931.0011/18/20140780110071213500
840471954PhDMarried65324.00111/1/201403840102213253629400
994771954PhDMarried65324.00111/1/201403840102213253629400
IdYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthResponseComplain
2230336319742n CycleMarried20130.0003/17/20149906376121103800
223185951973GraduationWidow42429.00111/2/20149955062042113500
223272321973GraduationWidow42429.00111/2/20149955062042113500
2233782919002n CycleDivorced36640.0109/26/201399156874251212501
223499771973GraduationDivorced78901.0019/17/201399321113093326343935400
2235101421976PhDDivorced66476.0017/3/2013993721812647487825211400
2236526319772n CycleMarried31056.0101/22/2013995101338161103800
2237221976GraduationDivorced46310.0103/12/201299185288155142615800
22385281978GraduationMarried65819.00011/29/201299267387011491656315410300
223940701969PhDMarried94871.0021/9/2012991692455318801441854710